{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"CNN2c.ipynb","provenance":[{"file_id":"11CKvMWbYn2HYpLAfu2xRhNmw9HDnkG5B","timestamp":1575826262900},{"file_id":"1nbofAi2dLnhJvkcLl1Xag7I1vx4MPR2s","timestamp":1575818931262},{"file_id":"1tTQLWB1ve20LC-Cxir5CrLKQ_vDyzeI2","timestamp":1575815207334}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"UxCpGqdfnQ2p","colab_type":"text"},"source":["# CNN2c SETTINGS\n","\n","SUBJECT'S DATA SELECTION:                                                                                                  \n","- **A**: Signal samples: 7794, EEG channels: 64, Testset letters: 100                                                           \n","- **B**: Signal samples: 7794, EEG channels: 64, Testset letters: 100                                                              \n","                                                                                                                                                   \n","ELECTRODE SUBSET SELECTION:                                                                                                \n","- **F**: Frontal lobe electrodes                                                                                                   \n","- **C**: Central lobe electrodes                                                                                               \n","- **P**: Parietal lobe electrodes                                                                                             \n","- **O**: Occipital lobe electrodes                                                                                            \n","- **LT**: Left temporal lobe electrodes                                                                                       \n","- **RT**: Right temporal lobe electrodes "]},{"cell_type":"code","metadata":{"id":"9nw7AE3rLWlk","colab_type":"code","outputId":"48f956af-5332-407b-afd0-6370658bf048","executionInfo":{"status":"ok","timestamp":1576426119179,"user_tz":-60,"elapsed":27410,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":333}},"source":["import matplotlib.pyplot as plt\n","import seaborn as sns\n","import pandas as pd\n","import numpy as np     \n","import random           \n","from scipy.io import loadmat\n","from scipy import signal\n","from google.colab import drive\n","import warnings\n","import string\n","import os\n","\n","from keras.layers import *\n","from keras.models import Model\n","from keras import backend as K\n","from keras import Sequential\n","from keras.callbacks import ModelCheckpoint\n","from keras.callbacks import EarlyStopping\n","from sklearn.metrics import confusion_matrix\n","from sklearn.preprocessing import scale\n","from sklearn.preprocessing import MinMaxScaler\n","\n","# Install mne library for topoplot\n","!pip install mne\n","import mne\n","\n","warnings.filterwarnings('ignore')\n","drive.mount('/content/drive')\n","\n","####################### SETTINGS ########################\n","# Set this variable with the desidered subject's letter #\n","SUBJECT_SELECTED = \"B\"                                  #\n","#                                                       #\n","# Set this variable with the desired electrode subset   #\n","ELECTRODE_SELECTED = \"O\"                                #\n","#########################################################\n","\n","subject_names = [\"A\", \"B\"]\n","electrode_names = [\"F\", \"C\", \"P\", \"O\", \"LT\", \"RT\"]\n","\n","# Check for errors in the settings\n","if SUBJECT_SELECTED not in subject_names:\n","    raise ValueError(\"SUBJECT_SELECTED value {} is invalid.\\nPlease enter one of the following parameters {}\".format(SUBJECT_SELECTED, subject_names))\n","elif ELECTRODE_SELECTED not in electrode_names:\n","    raise ValueError(\"ELECTRODE_SELECTED value {} is invalid\\nPlease enter one of the following parameters {}\".format(ELECTRODE_SELECTED, electrodes_names))\n","\n","# Google drive data paths\n","MODEL_LOCATIONS_FILE_PATH = 'drive/My Drive/AY1920_DT_P300_SPELLER_03/Trained_models/CNN2c/' + SUBJECT_SELECTED \n","SUBJECT_TRAIN_FILE_PATH = 'drive/My Drive/AY1920_DT_P300_SPELLER_03/Dataset/Subject_' + SUBJECT_SELECTED + '_Train.mat'\n","SUBJECT_TEST_FILE_PATH = 'drive/My Drive/AY1920_DT_P300_SPELLER_03/Dataset/Subject_' + SUBJECT_SELECTED + '_Test.mat'\n","CHANNEL_LOCATIONS_FILE_PATH = 'drive/My Drive/AY1920_DT_P300_SPELLER_03/Dataset/channels.csv'\n","CHANNEL_COORD = 'drive/My Drive/AY1920_DT_P300_SPELLER_03/Dataset/coordinates.csv'\n","\n","# Channel selection\n","if ELECTRODE_SELECTED == \"F\":\n","    CHANNELS = [21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37]\n","elif ELECTRODE_SELECTED == \"C\":\n","    CHANNELS = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]\n","elif ELECTRODE_SELECTED == \"P\":\n","    CHANNELS = [15, 16, 17, 18, 19, 48, 49, 50, 51, 52]\n","elif ELECTRODE_SELECTED == \"O\":\n","    CHANNELS = [55, 56, 57, 58, 59, 60, 61, 62]\n","elif ELECTRODE_SELECTED == \"LT\":\n","    CHANNELS = [14, 38, 40, 44, 46, 47]\n","elif ELECTRODE_SELECTED == \"RT\":\n","    CHANNELS = [20, 39, 41, 45, 53, 54]"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Using TensorFlow backend.\n"],"name":"stderr"},{"output_type":"display_data","data":{"text/html":["<p style=\"color: red;\">\n","The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n","We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n","or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n","<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Collecting mne\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a1/7c/ad1b52a3fdd4be8f55e183f1eff7d76f48cd1bee83c5630f9c26770e032e/mne-0.19.2-py3-none-any.whl (6.4MB)\n","\u001b[K     |████████████████████████████████| 6.4MB 8.6MB/s \n","\u001b[?25hRequirement already satisfied: scipy>=0.17.1 in /usr/local/lib/python3.6/dist-packages (from mne) (1.3.3)\n","Requirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.6/dist-packages (from mne) (1.17.4)\n","Installing collected packages: mne\n","Successfully installed mne-0.19.2\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/numba/decorators.py:146: RuntimeWarning: Caching is not available when the 'parallel' target is in use. Caching is now being disabled to allow execution to continue.\n","  warnings.warn(msg, RuntimeWarning)\n"],"name":"stderr"},{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"CVrJuHCMFCSl","colab_type":"text"},"source":["# Training set processing\n","\n","1.   Application of bandpass filter **(0.1-20Hz)**;\n","2.   Down-sampling signals from 240Hz to 120Hz;\n","3.   Obtain windows of 650ms at the start of every flashing (175ms)\n","4.   Normalization of samples in each window: **Zi = (Xi - mu) / sigma**;\n","5.   Reshape each window to be a 3D tensor with dimensions: **(N_SAMPLES, 78, N_CHANNELS)**;\n","6.   Obtain **(noP300 / P300)** class ratio to balance out dataset during training;\n"]},{"cell_type":"code","metadata":{"id":"78aU0w6-Lp0y","colab_type":"code","outputId":"e199b20c-024e-4056-9fd1-614e321b1945","executionInfo":{"status":"ok","timestamp":1576426121049,"user_tz":-60,"elapsed":29258,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":134}},"source":["if not os.path.exists(SUBJECT_TRAIN_FILE_PATH):\n","    print(\"Missing file: {}\".format(SUBJECT_TRAIN_FILE_PATH))\n","else:\n","    # Load the required data\n","    data = loadmat(SUBJECT_TRAIN_FILE_PATH)\n","    # Get the variables of interest from the loaded dictionary\n","    signals = data['Signal']\n","    # Take only the 8 selected channels of the signal\n","    signals = signals[:, :, CHANNELS]\n","    \n","    flashing = data['Flashing']\n","    stimulus = data['StimulusType']\n","    word = data['TargetChar']\n","    \n","    SAMPLING_FREQUENCY = 240\n","    # From the dataset description we know that there are 15 repetitions\n","    REPETITIONS = 15\n","    # Compute the duration of the recording in minutes\n","    RECORDING_DURATION = (len(signals))*(len(signals[0]))/(SAMPLING_FREQUENCY*60)\n","    # Compute number of trials\n","    TRIALS = len(word[0])\n","    # Set flag to True to balance the training set\n","    BALANCE_DATASET = False\n","\n","    print(\"**********************************\")\n","    print(\"       TRAIN SET INFORMATION      \")\n","    print(\"**********************************\")\n","    print(\"Sampling Frequency: %d Hz [%.2f ms]\" % (SAMPLING_FREQUENCY, (1000/SAMPLING_FREQUENCY)))\n","    print(\"Session duration:   %.2f min\" % RECORDING_DURATION)\n","    print(\"Number of letters:  %d\" % TRIALS)\n","    print(\"Spelled word:       %s\" % ''.join(word))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["**********************************\n","       TRAIN SET INFORMATION      \n","**********************************\n","Sampling Frequency: 240 Hz [4.17 ms]\n","Session duration:   46.01 min\n","Number of letters:  85\n","Spelled word:       VGREAAH8TVRHBYN_UGCOLO4EUERDOOHCIFOMDNU6LQCPKEIREKOYRQIDJXPBKOJDWZEUEWWFOEBHXTQTTZUMO\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"IXuVC7puKcLt","colab_type":"code","colab":{}},"source":["# Application of butterworth filter\n","b, a = signal.butter(4, [0.1/SAMPLING_FREQUENCY, 20/SAMPLING_FREQUENCY], 'bandpass')\n","for trial in range(TRIALS):\n","    signals[trial, :, :] = signal.filtfilt(b, a, signals[trial, :, :], axis=0)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"jS1FuKDuixnF","colab_type":"code","outputId":"a19bb4fb-6a4b-4b44-ba44-7f729431d9cb","executionInfo":{"status":"ok","timestamp":1576426121353,"user_tz":-60,"elapsed":29535,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":50}},"source":["# Down-sampling of the signals from 240Hz to 120Hz\n","DOWNSAMPLING_FREQUENCY = 120\n","SCALE_FACTOR = round(SAMPLING_FREQUENCY / DOWNSAMPLING_FREQUENCY)\n","SAMPLING_FREQUENCY = DOWNSAMPLING_FREQUENCY\n","\n","print(\"# Samples of EEG signals before downsampling: {}\".format(len(signals[0])))\n","\n","signals = signals[:, 0:-1:SCALE_FACTOR, :]\n","flashing = flashing[:, 0:-1:SCALE_FACTOR]\n","stimulus = stimulus[:, 0:-1:SCALE_FACTOR]\n","\n","print(\"# Samples of EEG signals after downsampling: {}\".format(len(signals[0])))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["# Samples of EEG signals before downsampling: 7794\n","# Samples of EEG signals after downsampling: 3897\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"bGaRKwu9MIvP","colab_type":"code","outputId":"3811eb93-0287-45ca-c7dc-4983d900ecd5","executionInfo":{"status":"ok","timestamp":1576426122926,"user_tz":-60,"elapsed":31095,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":33}},"source":["# Number of EEG channels\n","N_CHANNELS = 8\n","# Window duration after each flashing [ms]\n","WINDOW_DURATION = 650\n","# Number of samples of each window\n","WINDOW_SAMPLES = round(SAMPLING_FREQUENCY * (WINDOW_DURATION / 1000))\n","# Number of samples for each character in trials\n","SAMPLES_PER_TRIAL = len(signals[0])\n","\n","train_features = []\n","train_labels = []\n","\n","count_positive = 0\n","count_negative = 0\n","\n","for trial in range(TRIALS):\n","    for sample in (range(SAMPLES_PER_TRIAL)):\n","        if (sample == 0) or (flashing[trial, sample-1] == 0 and flashing[trial, sample] == 1):\n","            lower_sample = sample\n","            upper_sample = sample + WINDOW_SAMPLES\n","            window = signals[trial, lower_sample:upper_sample, :]                \n","            # Features extraction\n","            train_features.append(window)\n","            # Labels extraction\n","            if stimulus[trial, sample] == 1:\n","                count_positive += 1\n","                train_labels.append(1) # Class P300\n","            else:\n","                count_negative += 1\n","                train_labels.append(0) # Class no-P300\n","\n","# Get negative-positive classes ratio\n","train_ratio = count_negative/count_positive\n","\n","# Convert lists to numpy arrays\n","train_features = np.array(train_features)\n","train_labels = np.array(train_labels)\n","\n","# 3D Tensor shape (SAMPLES, 78, 8)\n","dim_train = train_features.shape\n","print(\"Features tensor shape: {}\".format(dim_train))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Features tensor shape: (15300, 78, 8)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"kKMlV1Mqapew","colab_type":"code","colab":{}},"source":["# Data normalization Zi = (Xi - mu) / sigma\n","for pattern in range(len(train_features)):\n","    train_features[pattern] = scale(train_features[pattern], axis=0)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"xTVrfKzJH2aE","colab_type":"text"},"source":["# Testing set processing\n","\n","1.   Application of bandpass filter **(0.1-20Hz)**;\n","2.   Down-sampling signas from 240Hz to 120Hz;\n","3.   Obtain windows of 650ms at the start of every flashing (175ms)\n","4.   Normalization of samples in each window: **Zi = (Xi - mu) / sigma**;\n","5.   Reshape each window to be a 3D tensor with dimensions: **(N_SAMPLES, 78, N_CHANNELS)**;\n","6.   Calculate weights vector to balance samples importance and obtain correct accuracy estimation;"]},{"cell_type":"code","metadata":{"id":"Nm-bZZsKTliz","colab_type":"code","outputId":"377bce66-9fc9-4587-fc47-1921b8ba20f5","executionInfo":{"status":"ok","timestamp":1576426130954,"user_tz":-60,"elapsed":39099,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":134}},"source":["# Test data loading\n","if not os.path.exists(SUBJECT_TEST_FILE_PATH):\n","    print(\"Missing file: {}\", SUBJECT_TEST_FILE_PATH)\n","else:\n","    # Load the required data\n","    data_test = loadmat(SUBJECT_TEST_FILE_PATH)\n","    # Get the variables of interest from the loaded dictionary\n","    signals_test = data_test['Signal']\n","    signals_test = signals_test[:, :, CHANNELS]\n","    flashing_test = data_test['Flashing']\n","    word_test =  data_test['TargetChar']\n","    stimulus_code_test = data_test['StimulusCode']\n","\n","    SAMPLING_FREQUENCY = 240\n","    # From the dataset description we know that there are 15 repetitions\n","    REPETITIONS = 15\n","    # Compute the duration of the recording in minutes\n","    RECORDING_DURATION_TEST = (len(signals_test))*(len(signals_test[0]))/(SAMPLING_FREQUENCY*60)\n","    # Compute number of trials\n","    TRIALS_TEST = len(word_test[0])\n","    # Number of samples for each character in trials\n","    SAMPLES_PER_TRIAL_TEST = len(signals_test[0])\n","    \n","    print(\"**********************************\")\n","    print(\"        TEST SET INFORMATION      \")\n","    print(\"**********************************\")\n","    print(\"Sampling Frequency: %d Hz [%.2f ms]\" % (SAMPLING_FREQUENCY, (1000/SAMPLING_FREQUENCY)))\n","    print(\"Session duration:   %.2f min\" % RECORDING_DURATION_TEST)\n","    print(\"Number of letters:  %d\" % TRIALS_TEST)\n","    print(\"Spelled word:       %s\" % ''.join(word_test))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["**********************************\n","        TEST SET INFORMATION      \n","**********************************\n","Sampling Frequency: 240 Hz [4.17 ms]\n","Session duration:   54.12 min\n","Number of letters:  100\n","Spelled word:       MERMIROOMUHJPXJOHUVLEORZP3GLOO7AUFDKEFTWEOOALZOP9ROCGZET1Y19EWX65QUYU7NAK_4YCJDVDNGQXODBEV2B5EFDIDNR\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"SvCresQJm25A","colab_type":"code","colab":{}},"source":["# Create characters matrix\n","char_matrix = [[0 for j in range(6)] for i in range(6)]\n","s = string.ascii_uppercase + '1' + '2' + '3' + '4' + '5' + '6' + '7' + '8' + '9' + '_'\n","\n","# Append cols and rows in a list\n","list_matrix = []\n","for i in range(6):\n","    col = [s[j] for j in range(i, 36, 6)]\n","    list_matrix.append(col)\n","for i in range(6):\n","    row = [s[j] for j in range(i * 6, i * 6 + 6)]\n","    list_matrix.append(row)\n","\n","# Create StimulusType array for the test set (missing from the given database)\n","stimulus_test = [[0 for j in range(SAMPLES_PER_TRIAL_TEST)] for i in range(TRIALS_TEST)]\n","stimulus_test = np.array(stimulus_test)\n","\n","for trial in range(TRIALS_TEST):\n","    counter=0\n","    for sample in range(SAMPLES_PER_TRIAL_TEST):\n","        index = int(stimulus_code_test[trial, sample]) - 1\n","        if not index == -1:\n","            if word_test[0][trial] in list_matrix[index]:\n","                stimulus_test[trial, sample] = 1\n","            else:\n","                stimulus_test[trial, sample] = 0"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"UNy5oI8ooMAw","colab_type":"code","colab":{}},"source":["# Application of butterworth filter\n","b, a = signal.butter(4, [0.1/SAMPLING_FREQUENCY, 20/SAMPLING_FREQUENCY], 'bandpass')\n","for trial in range(TRIALS_TEST):\n","    signals_test[trial, :, :] = signal.filtfilt(b, a, signals_test[trial, :, :], axis=0)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"8CpKeth5qOjr","colab_type":"code","outputId":"241a49a6-0ae9-4691-a35a-4422b5671d5e","executionInfo":{"status":"ok","timestamp":1576426132039,"user_tz":-60,"elapsed":40139,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":50}},"source":["# Down-sampling of the signals from 240Hz to 120Hz\n","DOWNSAMPLING_FREQUENCY = 120\n","SCALE_FACTOR = round(SAMPLING_FREQUENCY / DOWNSAMPLING_FREQUENCY)\n","SAMPLING_FREQUENCY = DOWNSAMPLING_FREQUENCY\n","\n","print(\"# Samples of EEG signals before downsampling: {}\".format(len(signals_test[0])))\n","\n","signals_test = signals_test[:, 0:-1:SCALE_FACTOR, :]\n","flashing_test = flashing_test[:, 0:-1:SCALE_FACTOR]\n","stimulus_test = stimulus_test[:, 0:-1:SCALE_FACTOR]\n","\n","print(\"# Samples of EEG signals after downsampling: {}\".format(len(signals_test[0])))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["# Samples of EEG signals before downsampling: 7794\n","# Samples of EEG signals after downsampling: 3897\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"48sta55OVXFy","colab_type":"code","outputId":"918c5a3c-2b95-45a9-e6fe-9a9f24f7fd81","executionInfo":{"status":"ok","timestamp":1576426133421,"user_tz":-60,"elapsed":41506,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":33}},"source":["# Number of EEG channels\n","N_CHANNELS = len(CHANNELS)\n","# Window duration after each flashing [ms]\n","WINDOW_DURATION = 650\n","# Number of samples of each window\n","WINDOW_SAMPLES = round(SAMPLING_FREQUENCY * (WINDOW_DURATION / 1000))\n","# Number of samples for each character in trials\n","SAMPLES_PER_TRIAL_TEST = len(signals[0])\n","\n","test_features = []\n","test_labels = []\n","windowed_stimulus = []\n","\n","count_positive = 0\n","count_negative = 0\n","\n","for trial in range(TRIALS_TEST):\n","    for sample in (range(SAMPLES_PER_TRIAL_TEST)):\n","        if (sample == 0) or (flashing_test[trial, sample-1] == 0 and flashing_test[trial, sample] == 1):\n","            lower_sample = sample\n","            upper_sample = sample + WINDOW_SAMPLES\n","            window = signals_test[trial, lower_sample:upper_sample, :]\n","            # Extracting number of row/col in a window\n","            number_stimulus = int(stimulus_code_test[trial, sample])\n","            windowed_stimulus.append(number_stimulus)\n","            # Features extraction\n","            test_features.append(window)\n","            # Labels extraction\n","            if stimulus_test[trial, sample] == 1:\n","                count_positive += 1\n","                test_labels.append(1) # Class P300\n","            else:\n","                count_negative += 1\n","                test_labels.append(0) # Class no-P300\n","\n","# Get test weights to take into account the number of classes \n","test_weights = []\n","for i in range(len(test_labels)):\n","    if test_labels[i] == 1:\n","        test_weights.append(len(test_labels)/count_positive)\n","    else:\n","        test_weights.append(len(test_labels)/count_negative)\n","test_weights = np.array(test_weights)\n","\n","# Convert lists to numpy arrays\n","test_features = np.array(test_features)\n","test_labels = np.array(test_labels)\n","\n","# 3D tensor (SAMPLES, 78, 64)\n","dim_test = test_features.shape\n","print(\"Features tensor shape: {}\".format(dim_test))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Features tensor shape: (18000, 78, 8)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"-VY8Nr04YI9F","colab_type":"code","colab":{}},"source":["# Data normalization Zi = (Xi - mu) / sigma\n","for pattern in range(len(test_features)):\n","    test_features[pattern] = scale(test_features[pattern], axis=0)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"-VK4MFXTjHoM","colab_type":"text"},"source":["# CNN2c model definition, training and testing\n","\n","1.   Function definiton for randomization of weights and biases;\n","2.   **Scaled_tanh(x)** activation function definition;\n","3.   ANN model definition (2 Conv1D layers, 2 dense layers);\n","4.   Training of the network over weighted dataset;\n","5.   CNN2c performance assessment;"]},{"cell_type":"code","metadata":{"id":"eTIc_w_TcOmK","colab_type":"code","colab":{}},"source":["# Randomizing function for bias and weights of the network\n","def cecotti_normal(shape, dtype = None, partition_info = None):\n","    if len(shape) == 1:\n","        fan_in = shape[0]\n","    elif len(shape) == 2:\n","        fan_in = shape[0]\n","    else:\n","        receptive_field_size = 1\n","        for dim in shape[:-2]:\n","            receptive_field_size *= dim\n","        fan_in = shape[-2] * receptive_field_size\n","    return K.random_normal(shape, mean = 0.0, stddev = (1.0 / fan_in))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"VeyyvqGgcTsa","colab_type":"code","colab":{}},"source":["# Custom tanh activation function\n","def scaled_tanh(z):\n","    return 1.7159 * K.tanh((2.0 / 3.0) * z)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"BuMCOgJ3ST-h","colab_type":"code","colab":{}},"source":["# Build the model\n","def CNN2c_model(channels=N_CHANNELS, filters=10):\n","    model = Sequential([\n","        Conv1D(\n","            filters = filters,\n","            kernel_size = 1,\n","            padding = \"same\",\n","            bias_initializer = cecotti_normal,\n","            kernel_initializer = cecotti_normal,\n","            use_bias = True,\n","            activation = scaled_tanh,\n","            input_shape = (78, channels)\n","        ),\n","        Conv1D(\n","            filters = 50,\n","            kernel_size = 13,\n","            padding = \"valid\",\n","            strides = 11,\n","            bias_initializer = cecotti_normal,\n","            kernel_initializer = cecotti_normal,\n","            use_bias = True,\n","            activation = scaled_tanh,\n","        ),\n","        Flatten(),\n","        Dense(100, activation=\"sigmoid\"),\n","        Dense(1, activation=\"sigmoid\")\n","    ])\n","    model.compile(optimizer = 'adam', loss = 'mean_squared_error', metrics = ['accuracy'])\n","    return model"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"jVMdFs4quaZL","colab_type":"code","outputId":"9e9f5f4e-d892-4751-e485-0156b444b202","executionInfo":{"status":"ok","timestamp":1576426140240,"user_tz":-60,"elapsed":48258,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":505}},"source":["# Training parameters\n","BATCH_SIZE = 256\n","EPOCHS = 200\n","VALID_SPLIT = 0.05\n","SHUFFLE = 1 # set to 1 to shuffle subsets during training\n","\n","# Model summary\n","CNN2c_model(channels=N_CHANNELS, filters=10).summary()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4409: The name tf.random_normal is deprecated. Please use tf.random.normal instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n","\n","Model: \"sequential_1\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","conv1d_1 (Conv1D)            (None, 78, 10)            90        \n","_________________________________________________________________\n","conv1d_2 (Conv1D)            (None, 6, 50)             6550      \n","_________________________________________________________________\n","flatten_1 (Flatten)          (None, 300)               0         \n","_________________________________________________________________\n","dense_1 (Dense)              (None, 100)               30100     \n","_________________________________________________________________\n","dense_2 (Dense)              (None, 1)                 101       \n","=================================================================\n","Total params: 36,841\n","Trainable params: 36,841\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"GEPpgyxyTl-u","colab_type":"code","outputId":"c6f258b6-69d9-4dbf-e8b3-94b4bd07e276","executionInfo":{"status":"ok","timestamp":1576426172008,"user_tz":-60,"elapsed":80012,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# Model definition\n","model = CNN2c_model(channels=N_CHANNELS, filters=10)\n","\n","# Callback to save best model only\n","checkpoint = ModelCheckpoint(filepath=MODEL_LOCATIONS_FILE_PATH +  \"/model\" + ELECTRODE_SELECTED + \".h5\", \n","                             monitor='val_loss', \n","                             mode='min',\n","                             save_best_only=True)\n","\n","# Callback to stop when loss on validation set doesn't decrease in 50 epochs\n","earlystop = EarlyStopping(monitor = 'val_loss', \n","                              mode = 'min', \n","                              patience = 50, \n","                              restore_best_weights = True)\n","\n","# Callback to keep track of model statistics\n","history = model.fit(x=train_features, \n","                    y=train_labels, \n","                    batch_size=BATCH_SIZE, \n","                    epochs=EPOCHS, \n","                    validation_split=VALID_SPLIT, \n","                    callbacks=[checkpoint, earlystop],\n","                    shuffle=SHUFFLE,\n","                    class_weight={0: 1., 1: train_ratio})"],"execution_count":0,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3005: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n","\n","Train on 14535 samples, validate on 765 samples\n","Epoch 1/200\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n","\n","14535/14535 [==============================] - 8s 547us/step - loss: 0.3707 - acc: 0.6114 - val_loss: 0.2927 - val_acc: 0.7007\n","Epoch 2/200\n","14535/14535 [==============================] - 0s 19us/step - loss: 0.3202 - acc: 0.7232 - val_loss: 0.2694 - val_acc: 0.7346\n","Epoch 3/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.3130 - acc: 0.7238 - val_loss: 0.2662 - val_acc: 0.7634\n","Epoch 4/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.3117 - acc: 0.7322 - val_loss: 0.2665 - val_acc: 0.7333\n","Epoch 5/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.3081 - acc: 0.7370 - val_loss: 0.2658 - val_acc: 0.7412\n","Epoch 6/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.3057 - acc: 0.7359 - val_loss: 0.2637 - val_acc: 0.7294\n","Epoch 7/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.3043 - acc: 0.7367 - val_loss: 0.2618 - val_acc: 0.7582\n","Epoch 8/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.3047 - acc: 0.7346 - val_loss: 0.2617 - val_acc: 0.7673\n","Epoch 9/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.3033 - acc: 0.7399 - val_loss: 0.2635 - val_acc: 0.7542\n","Epoch 10/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.3045 - acc: 0.7395 - val_loss: 0.2629 - val_acc: 0.7373\n","Epoch 11/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.3023 - acc: 0.7329 - val_loss: 0.2597 - val_acc: 0.7869\n","Epoch 12/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.3026 - acc: 0.7365 - val_loss: 0.2626 - val_acc: 0.7242\n","Epoch 13/200\n","14535/14535 [==============================] - 0s 21us/step - loss: 0.3009 - acc: 0.7375 - val_loss: 0.2659 - val_acc: 0.7216\n","Epoch 14/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2995 - acc: 0.7415 - val_loss: 0.2581 - val_acc: 0.7386\n","Epoch 15/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2992 - acc: 0.7384 - val_loss: 0.2582 - val_acc: 0.7556\n","Epoch 16/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2988 - acc: 0.7424 - val_loss: 0.2620 - val_acc: 0.7294\n","Epoch 17/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2975 - acc: 0.7394 - val_loss: 0.2601 - val_acc: 0.7699\n","Epoch 18/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2967 - acc: 0.7471 - val_loss: 0.2600 - val_acc: 0.7294\n","Epoch 19/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.2961 - acc: 0.7382 - val_loss: 0.2587 - val_acc: 0.7477\n","Epoch 20/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.2939 - acc: 0.7496 - val_loss: 0.2580 - val_acc: 0.7464\n","Epoch 21/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2918 - acc: 0.7470 - val_loss: 0.2585 - val_acc: 0.7307\n","Epoch 22/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.2930 - acc: 0.7392 - val_loss: 0.2541 - val_acc: 0.7542\n","Epoch 23/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2921 - acc: 0.7443 - val_loss: 0.2577 - val_acc: 0.7176\n","Epoch 24/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2896 - acc: 0.7464 - val_loss: 0.2571 - val_acc: 0.7739\n","Epoch 25/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2894 - acc: 0.7480 - val_loss: 0.2564 - val_acc: 0.7490\n","Epoch 26/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.2873 - acc: 0.7518 - val_loss: 0.2555 - val_acc: 0.7242\n","Epoch 27/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2874 - acc: 0.7441 - val_loss: 0.2561 - val_acc: 0.7778\n","Epoch 28/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2851 - acc: 0.7544 - val_loss: 0.2574 - val_acc: 0.7098\n","Epoch 29/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2841 - acc: 0.7481 - val_loss: 0.2560 - val_acc: 0.7399\n","Epoch 30/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.2845 - acc: 0.7567 - val_loss: 0.2542 - val_acc: 0.7373\n","Epoch 31/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.2811 - acc: 0.7576 - val_loss: 0.2557 - val_acc: 0.7163\n","Epoch 32/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2794 - acc: 0.7553 - val_loss: 0.2569 - val_acc: 0.7438\n","Epoch 33/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2785 - acc: 0.7582 - val_loss: 0.2592 - val_acc: 0.7294\n","Epoch 34/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.2764 - acc: 0.7579 - val_loss: 0.2536 - val_acc: 0.7412\n","Epoch 35/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2745 - acc: 0.7648 - val_loss: 0.2608 - val_acc: 0.7059\n","Epoch 36/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.2749 - acc: 0.7591 - val_loss: 0.2549 - val_acc: 0.7386\n","Epoch 37/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2715 - acc: 0.7644 - val_loss: 0.2524 - val_acc: 0.7725\n","Epoch 38/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.2701 - acc: 0.7691 - val_loss: 0.2552 - val_acc: 0.7569\n","Epoch 39/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.2690 - acc: 0.7666 - val_loss: 0.2598 - val_acc: 0.7203\n","Epoch 40/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2653 - acc: 0.7718 - val_loss: 0.2555 - val_acc: 0.7373\n","Epoch 41/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2629 - acc: 0.7724 - val_loss: 0.2550 - val_acc: 0.7621\n","Epoch 42/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2604 - acc: 0.7758 - val_loss: 0.2580 - val_acc: 0.7386\n","Epoch 43/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2568 - acc: 0.7815 - val_loss: 0.2617 - val_acc: 0.7647\n","Epoch 44/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.2541 - acc: 0.7812 - val_loss: 0.2582 - val_acc: 0.7686\n","Epoch 45/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2514 - acc: 0.7874 - val_loss: 0.2584 - val_acc: 0.7399\n","Epoch 46/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2490 - acc: 0.7871 - val_loss: 0.2608 - val_acc: 0.7451\n","Epoch 47/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.2447 - acc: 0.7890 - val_loss: 0.2623 - val_acc: 0.7739\n","Epoch 48/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.2429 - acc: 0.7953 - val_loss: 0.2545 - val_acc: 0.7569\n","Epoch 49/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.2376 - acc: 0.7954 - val_loss: 0.2645 - val_acc: 0.7556\n","Epoch 50/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2329 - acc: 0.8049 - val_loss: 0.2630 - val_acc: 0.7216\n","Epoch 51/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.2295 - acc: 0.8068 - val_loss: 0.2585 - val_acc: 0.7503\n","Epoch 52/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.2253 - acc: 0.8092 - val_loss: 0.2625 - val_acc: 0.7621\n","Epoch 53/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.2215 - acc: 0.8151 - val_loss: 0.2604 - val_acc: 0.7569\n","Epoch 54/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2157 - acc: 0.8197 - val_loss: 0.2639 - val_acc: 0.7386\n","Epoch 55/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2124 - acc: 0.8240 - val_loss: 0.2691 - val_acc: 0.7373\n","Epoch 56/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.2085 - acc: 0.8257 - val_loss: 0.2691 - val_acc: 0.7490\n","Epoch 57/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.2049 - acc: 0.8306 - val_loss: 0.2615 - val_acc: 0.7516\n","Epoch 58/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.1990 - acc: 0.8371 - val_loss: 0.2674 - val_acc: 0.7346\n","Epoch 59/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.1949 - acc: 0.8394 - val_loss: 0.2731 - val_acc: 0.7608\n","Epoch 60/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.1895 - acc: 0.8466 - val_loss: 0.2752 - val_acc: 0.7556\n","Epoch 61/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.1856 - acc: 0.8550 - val_loss: 0.2703 - val_acc: 0.7582\n","Epoch 62/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.1804 - acc: 0.8540 - val_loss: 0.2769 - val_acc: 0.7621\n","Epoch 63/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.1763 - acc: 0.8605 - val_loss: 0.2775 - val_acc: 0.7386\n","Epoch 64/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.1723 - acc: 0.8651 - val_loss: 0.2766 - val_acc: 0.7451\n","Epoch 65/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.1669 - acc: 0.8666 - val_loss: 0.2779 - val_acc: 0.7608\n","Epoch 66/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.1621 - acc: 0.8744 - val_loss: 0.2760 - val_acc: 0.7582\n","Epoch 67/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.1580 - acc: 0.8793 - val_loss: 0.2731 - val_acc: 0.7647\n","Epoch 68/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.1541 - acc: 0.8809 - val_loss: 0.2732 - val_acc: 0.7725\n","Epoch 69/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.1497 - acc: 0.8848 - val_loss: 0.2838 - val_acc: 0.7804\n","Epoch 70/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.1456 - acc: 0.8903 - val_loss: 0.2807 - val_acc: 0.7634\n","Epoch 71/200\n","14535/14535 [==============================] - 0s 18us/step - loss: 0.1416 - acc: 0.8956 - val_loss: 0.2851 - val_acc: 0.7804\n","Epoch 72/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.1376 - acc: 0.8967 - val_loss: 0.2863 - val_acc: 0.7895\n","Epoch 73/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.1329 - acc: 0.9005 - val_loss: 0.2903 - val_acc: 0.7752\n","Epoch 74/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.1292 - acc: 0.9047 - val_loss: 0.2818 - val_acc: 0.7686\n","Epoch 75/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.1255 - acc: 0.9089 - val_loss: 0.2845 - val_acc: 0.7791\n","Epoch 76/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.1214 - acc: 0.9118 - val_loss: 0.2852 - val_acc: 0.7699\n","Epoch 77/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.1181 - acc: 0.9139 - val_loss: 0.2934 - val_acc: 0.7935\n","Epoch 78/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.1155 - acc: 0.9150 - val_loss: 0.2904 - val_acc: 0.7908\n","Epoch 79/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.1120 - acc: 0.9191 - val_loss: 0.2952 - val_acc: 0.7987\n","Epoch 80/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.1069 - acc: 0.9238 - val_loss: 0.2926 - val_acc: 0.7791\n","Epoch 81/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.1040 - acc: 0.9275 - val_loss: 0.2874 - val_acc: 0.7843\n","Epoch 82/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.1008 - acc: 0.9278 - val_loss: 0.2991 - val_acc: 0.7895\n","Epoch 83/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.0981 - acc: 0.9307 - val_loss: 0.3060 - val_acc: 0.8065\n","Epoch 84/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.0952 - acc: 0.9327 - val_loss: 0.2925 - val_acc: 0.7856\n","Epoch 85/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.0919 - acc: 0.9357 - val_loss: 0.3022 - val_acc: 0.7935\n","Epoch 86/200\n","14535/14535 [==============================] - 0s 16us/step - loss: 0.0884 - acc: 0.9386 - val_loss: 0.3018 - val_acc: 0.8026\n","Epoch 87/200\n","14535/14535 [==============================] - 0s 17us/step - loss: 0.0856 - acc: 0.9418 - val_loss: 0.3040 - val_acc: 0.7908\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"eJHaj2f1VFYZ","colab_type":"code","outputId":"52053b60-0d1b-473d-87a0-7f0f59b490a9","executionInfo":{"status":"ok","timestamp":1576426172929,"user_tz":-60,"elapsed":80925,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":265}},"source":["plt.figure(figsize=(15,4))\n","plt.style.use('tableau-colorblind10')\n","\n","# Plots of loss curves during training\n","plt.subplot(1,2,1)\n","plt.plot(history.history['loss'], label=\"training loss\")\n","plt.plot(history.history['val_loss'], label=\"validation loss\")\n","plt.legend()\n","\n","# Plots of accuracy curves during training\n","plt.subplot(1,2,2)\n","plt.plot(history.history['acc'], label=\"training accuracy\")\n","plt.plot(history.history['val_acc'], label=\"validation accuracy\")\n","plt.legend()\n","\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA3AAAAD4CAYAAACt4QT/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nICsnD2dHe2uHI4QQQgghhLiFdp1M4vG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dqnz8QEtGf7+BFxdu5a37oqwdkhBCCCGEEOWG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9cnC1i45yRD+lxtLha3h071q/NRo72cy/Tl1jUBTN8aywsDI03z7Cp1ock1pvx7milgUiNuDe5K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\nJP4AtNXRC4id7wsAAAAASUVORK5CYII=\n","text/plain":["<Figure size 1080x288 with 2 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"Sg1zrX0xZTRt","colab_type":"code","colab":{}},"source":["# Define model\n","best_model = CNN2c_model(channels=N_CHANNELS, filters=10)\n","\n","# Load best model weights from h5 file\n","best_model.load_weights(MODEL_LOCATIONS_FILE_PATH + \"/model\" + ELECTRODE_SELECTED + \".h5\")\n","\n","# Compile best model model\n","best_model.compile(optimizer = 'adam', loss = 'mean_squared_error', metrics = ['accuracy'])"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"_j_iqu0tYlbS","colab_type":"code","outputId":"86aad8dd-9829-4688-a5bc-806873128295","executionInfo":{"status":"ok","timestamp":1576426175556,"user_tz":-60,"elapsed":83528,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":377}},"source":["# Load topoplot coordinates\n","if not os.path.exists(CHANNEL_COORD):\n","    print(\"Missing file: {}\".format(CHANNEL_COORD))\n","else:\n","    xycoord = []\n","    # Read file content\n","    with open(CHANNEL_COORD, \"r\") as f:\n","        file_content = f.read()\n","        # Loop over all rows\n","        for row in file_content.split(\"\\n\"): \n","        # Skip missing rows\n","            if row == '':\n","                continue\n","            xycoord.append(row)\n","\n","# Create a list with x,y coordinates of each electrodes well formatted\n","coord = []\n","for x in xycoord:\n","    coord.append(x.split(','))\n","coord = np.array(coord)\n","\n","# Flip y axis in order to plot in the right way the electrodes positions\n","for i in range(len(coord)):\n","    coord[i][1] = 681 -int(coord[i][1])\n","\n","# Create a list of weights for all filters\n","nf = []\n","for filt in range(10):\n","    nf.append(abs(np.array(best_model.get_weights()[0])[0, :, filt]))\n","nf = np.array(nf, dtype=float)\n","\n","# Add -1 to missing channels values\n","tmp_full = []\n","for i in range(10):\n","    tmp = np.full(64, 0, dtype=float)\n","    for c in range(N_CHANNELS):\n","        tmp[CHANNELS[c]] = nf[i][c]\n","    tmp_full.append(tmp)\n","nf = np.array(tmp_full, dtype=float)\n","\n","# Plot topoplot of the 10 filters in the first convolutional layer\n","fig = plt.figure(figsize=(15,6))\n","for i in range(10):\n","    ax = fig.add_subplot(2,5,i+1)\n","    ax.set_title(\"Filter #{}\".format(i+1))\n","    mne.viz.plot_topomap(nf[i], coord, cmap='Spectral_r', axes=ax, show=False)"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA1MAAAFoCAYAAACymqHbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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2TJDS27UZR7gqYzQ4w8xn3BjEc37PXOP1Y30ATNC0ClzMb/kAXfMFuMBtQZAvyshYj3V1y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\neoBAShvbsZDSQUqbsaledM1NfdWduA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EFIKXlBi5LKZ9LfJb9Qoj/nYSnfHPlDmXGfwR+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\nLmOT3bidRRkNI9NMrEv+QTbGVCg2yXe++/+GDSNHrv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JTs0bhwMgF6ivfMW+SV4SGRbfj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2RpzaNcHUBR1SaIKmQnuPf5bG7G0NUMRCooe5uCuB7GdLGcvPYIQCslEO8f3f3zFFzVkJti7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2OgwZxCBO9aRBCqHFd/YKmig/+5G3HRV93+oJQMpKIQSK8F7FoqMOv3CIJfAKcwMLxQ3tz\nO7BD4uSEdtEFN5T1VXJNKsPPOVtQsrRy9UmjmA8JlWsrmJaHWfKI5h2UQIaZDaaKHdWx4jpmyqeu\noURdo0VHjc/8JCxIeSxMlagzG4n7GgxtR27qYtvtW/nvv+zkfHv/rdEemWMXOU4VbdMet6THbfRw\nIZ38iR3O39ltuwRvCqT8y4t1TGbxzFBV/Z1CKN9ZtfyN5rIF56jiAKynAxnQ1XMvE/k9NNYuoq3p\nSEQkWiZRZZMKVVTJ1L7SpfC19pYvKYHE1xTsqIaalCRSLqm0TX1jifaYZE4C5iY85iacMBDYaIKR\nncjeHcgtPdiPDfDzu0YZ3gntozO7wEkp+S3buYxF0y6YUkruYU/wa7Y5Ej7lElxzKFecXmkQQpxp\noPzhdNqib2ChuTdhAviD7OI1zCMmptfj6hs1WtoM6lslXWKELX6O91x8ONqcNLTUI5rqIdUCNS0U\n/Rzf/s6NLDl2Ean58+ktaOzOQ09OMDIYY2QoitIfkB7M033P9aw+/LIXaxfsF32DT3Lf4z8sBoF3\nneuVPnKoyk1eiRBCLI+g3rqM2sZ3sCyWFAaPyWEiqBy+n6LAgiURapaUuHZkB9/85FmIpZ2I1oW4\n8RoyzgB9hYCdOZOurGB3AQZHTcaGo2TGTIyMRyxnY1phcLPmlYtV5euuE9Ww4jqZhhjJtEMk6pNI\nOcTiLqmER1IPOwIpPbQwj2mT86qmKjGVoJobpZdvlUu4ImS1A1XpPlS6FLYvsINwwW15CkVPsHH9\nLn704W8X8mPZ++yCddmh5H72SocQoimViv2pc0Hzimt/8a/xeQvr8QIHJ/CxfaXqLm37CnYQHlM3\nEJR8UY1Gcf0pYeP+FEIVTEY8VAx0gkBUJf5T7yE05gl8QclSq8VWwwyVLZVztzEe0BiB9ji0xDzm\nJmxqzTgpvRE1P4Yc3AHdvThP9fOp69dyhjaf/EZlxvmpLXKcDA7Hi+Zpj4/JEj9gQ7GXQq+Nf66U\nctcLfjAOAl4yZKoCIcTJcVO94fzVc2p+8JFXxWtbQ/cdEjFEJDGNTPkKVSLlBBZO4IT25o5KwQsJ\nVc4N7aKzLtNcpIoFvdrGL+Z1fEuEJMpyy/fhTZTFyr6u4BoqVkLHihsU6kzqGko0tRZor3NZUhMS\nqsU1JerMFqK2j9yzBTZ28YvrHsXfY1CzPc1EZl8m/3vZxQXMI7rXwuUXcgvrGS3kcB8q4b9FSjnw\nohyEWRwQhBCLNS1yQzrRtui0Y9+fSMSePv17b0gpGc10s2foKfzAQVNN6mrmUZNsIx6tx9fVSRI1\nZUZk6n31uSqD+HooFbTiBqW4jp4OSNfZNDQXaU35LEqFhGpByqYhUkNKqYWRbuSObvxNvViPDvHR\nP+/kuNF2tOLMDe51cgQdpbqgGZc2P+ap/C5yu0v4b5RSPnVAO2MWLwqEEA0R1Ovi6Ge8jyMS86dk\n+OwqF3hO26vAE4srtLQbtLSraHUWV2/bzDeuOAa9swbRUg8tDYh0M9S04Kjw6BNP8Jfb1nLe289n\nT1GnJ0914ToyGGNi0CA9UkR0bcfq28aCOSe92LsBANe1eGT9L4o79zxc8H3nzbO5Z4cmhBARA+XL\nKsq738bS2C5yXCoW7fN7zW06C44SfHNgI1/9yKkkVy8NiVSylnFnDz05wfasSVcWujMKQ/1xRgaj\naGM+qTGLaNbG7t3K4OgWHKcwKTGVkli0lnltx+PV15JPmxSTJoWkQTzlkki5xOIuiZRDIupXSVVc\nC0lVGLZbJlRqgDGFSIX307tUU+dkpkq+bF+QL0lu+N4tzs0/vsVxbfcDSPmz2YLVoQchhKLp6gd1\nXf3Cpz//WuOyK8/U7EArk+HQEM32BUVPoeSH4ycVIjXtVs7Fq5qguEpV0u95omxeFf5cuOX5TzdA\n88IAcsUPqjOhEK4bwnECjULSIJrwSNfZpOtKNKZd5sahMynpiDvMSQTUGM1EHB+GtyO391B6so9P\nXvs4pyudFDep+43/AbhRdnEOc0gJAyklDzIgf85WK0B+3SX4r5dSweolR6YAhBDxqKleberaW7/5\n8VfH3vKm41FjqWric4VI2X6hTKIsLM+vEql8mUhlnLAblSl3pCaKannoORx8rpCoaMElUnCmkagN\n639H39CTtDcdSTl8B4BEvInmzmNxmurJ1kXxmlSaWot0dBRYkoLltR6La2waI43EbB/ZtwW5YTuf\n+drdnM48xp5Q92HyGWlzF328VoQZRpb0+BM73Dvpc1yCDwDXz14oD20IIVQhlI8piva5I5dcYh62\n8DWqqu5rDf5s4Ho24xO7mMjvoWCNzfh7itBQFDVMRReCXH6QwdEtLFtwTpmMtRMk4lgJnXyNST4d\nId7s0tBk0d5cYn4CFtX4LEzZNEeT1OgNYUe1azv+hl767h/iU3/fydlD81BmCG+VUvJrtvF6FnAP\ne4Kb6LZ95Ne8UP/8krlQvlIhhLhMR/nxKbRG/oUFhonKr9nG5SxBmdJt1DRB2xyD5jadmmaXb3Zv\n5ANnL2HuiiaUtrqQSNWH8yh+JMZIboBPfvxa3nPVFQw5EXbnFXYXoD+jMTIUY2w4gjnkovf08MjN\n/8GJR72TlsYDjxx4PpBSsmvPIzz85PVFz3f+4PvOB2Yr+4c+hBCn6Cg3LqYm+XaWRRvE5Mxqbb3G\n8tUq3xl5io//r+PpOHUFomMpdizGuN3H1ozJlgmdrRPQMxBhoC9O0A+1QwXY1c3unofwfYf6dCfN\n9cuJmNMlprnCEDt6H6S79wGOWHIRzUecVSVVpZROIukQS3jle5dE1K8SqliZVEXU8Hu9LPcLu1RU\nbc8VsZfJSnk2xivPyzz+4Fa+84mfFsaHxtfaRfvyWROqQx9CiCWxuHlDx/zmRR+9+l3xjmWd5Vl+\nQcELSVTRm5zhLzhiSpi4iuOo0/LwXFvBcMquo15QjXPQHX+SRFk2Tn6MojWGZU+wecc/0BSD+tr5\nKEIjGkmRjDWRaFxAUJsmnzbJ1kWf9RrBebKHT/zwEU5VOimsV/eJgIVwLXsT3ZxBOz9jS2EXuQE7\nLLKuffGPwvPDS5JMVSCEOD6divxs/tyG9p9c8674quOOgEgKT5E4fhEnsLD9IgUvqBKpnKsy4YRD\ndpkqkRLksjrFvB7mmuQ13IJSJVHRQtiNMrIFdvQ9TCbXi67FMPQoyxecO22bJnL97B54DNsp0N55\nImLZ4Yw3xalpd+jozLKsLuCw2oDltRYt0TKh2rUR74ltfPjLd3NZ9DC61+y7xlwjh4igUMCTv2Rr\nCfhLAe8Ds92olxaEEAsNPf7fmmoed+LR74i1Nx/1gryOlBIpfYLAJ5ChhWrRGqO790EWzDmZXGGQ\n8YnduJ5FfbqTjvZjKdbGyDTGyDVEaGkv0NKeZ2GNZFlasrimRFs8Sq3RCgObkdu78J7oYe2d/fzk\n/qrRLVgAABI6SURBVCFOHpoz47bcL/dwA112gFxXwHu7lHLzC/JPz+IFgRCiIYb2DQlvPII680I6\nxRwxPZahsVmnbY5BXUvAXyZ2Mm9ugnPPXIDaWhMSqcZGqGlBJuopeON89t+v49WXn4Gsbaa/GM6k\n9OVDed/YSAR3RKFmpEjQvY2n1vyc0459X9XJ78VAJtvLg+uuLWRye4Zcr/h2KeU9L9qLz+J5QwgR\nNVE/I5EfPY952vnM1ZIxnZUnRvjRxHre89ZVLDlnJaJjKVbUZKQ0wMbxKJvGVTZPCHbvTDLcE6Wx\nL4e/6Ul6dz9MTbKVzvYT0bWnN96RUrJ+y5+wnFz4N4vPppSOUkwaFJImXkotS6c8YnF3H1IV2plD\ntPx1OB4uq9I/tUymKqKDipPfQH+GH33+V9Zjdz5Rsi3nvVLKG2aLrC8dlLtUV6qa9vXjLjhRv+BD\nl0WCeDIkU97k6MnUGf6KAYqwZBjZ4PiYlluNb9BLHl52lImJHiayfThusdpJVYRCNFJLLFpLzEwz\nOLqFdLKd5oblBNLHKmXIFYYYm9iJ4xRQVJ32piNRFx1GpjkB7YKW9gLzW0ssS8PSGoeFNQF1Zgda\nZhC5axve2i7+4wcPsdRug43mPoTKkh6/YKu7hqFAgc86BN96qRZZX9JkCsITUAjeaZrG11997jHq\n//nSOxLzF9dVu1JZR5S7UGr5XkwjUtm8Vs0zKRY07Kw6jURF8y5BZpSu3ffiuBYLOk4knep4xu0K\nAo+dfQ8zNrGL5tajUFeewMTcBHMXZFncYXFkHRxeW2JeMkHKM5G7nyL34FN87Op7uNw8nJ2PT2bo\nSSl5klGuY3PJxt9p479TSvngC7lfZ/HCQghxvqYaP0onO2pWH/HmZHP90n/KdkgpGRnvZvfA4+ia\nyaK5p+E1NzPamsBt0ejozFUvlsvTDgtSUB+ZgzK8E9m1FW/tTm69eQd3rS9yZH/LtOfullluYHtu\nJ9miQ/Be4I+zH+4vXQghVpso1yUxOi9jUXIljShCUFuv0dZRnpNSRlhbmuAjr18xOSfV3BjOSaWa\nKPo5rv/5raiJKItOXkVfQaOvAH1FGBqOMDYSZXzYJDVeIj1cZOu917Fy+b+gqk8fEn2wkM0Psm7T\n7wu7Bx4PAul/RsrgB7OBpi9dCCE6TdTva4jTPrp0WTSbyClvf8vRrLjgeMScw8kpNj35CTaOR3ly\nDLb2RejpTpHcbcHaR+nrfYSG2oV0th3Hgcy7VjCe7WXbrruY27qa2o4V5NMmhZRJMWkg4oREKumW\nQ3VdopHppCqiQqT8tVH23Jp06wsvpROjWX7//Vvsv/7yjgD4rmu7/ymlzB/kXTmLFwlCiFo9YnxZ\nwluPuux8ddHFFxuBkqoqpkpFDdtSy0X+6WMn7sQIIyNbyeR6kTJAIIhG0qRTHaSTHZjGcy9IeZ5N\n39ATjIzvwNBjdCw6jcLiedidBh2dOZa22hxZJ1leW6IjXkMyCNe1cv1WvvbtB4iPJWnoqqVkBdjS\n5w56/VvYaUu42cb/yEu9MfCSJ1MVCCESuq5+UNPUT736gqO1f/3EayJNC+ZUidSEozJaCgnUqA0T\nVpgTERIpnUI5GDKWs8P7rENprJeunvvQ9RgL555K1Ew984bsBSklfUNP0j+0nvYlp2OvXElkUcD8\nxROsbpAcWeewJG1QK2qRu54g++AGPvbVu7nCOILutQ7rGeVGugojlIZK+B8DbppdkL48IITQgLdp\nqvmlunRnfOXyN8Sb6pbs193mxUDJybF91z14vs3SzrOwO1oYmpMiOcdl/uIJDk9LDq/zOKzWpt6c\ng54ZRHZvwVvTxY03bWfdRo9l/Y10yQluoru4nYmSQ/AZ4Fop5f7D1GbxkoIIT87zI6hXpzE73hJb\nHL9ofjut7QrFRI5rd+3kK29ZjTEv7EjR0oCoaYFkEyXhcu+Dj3L3fZs59/87jz1FvUqkBseMyQH/\nEY9EpoSz9UmczCDzO058wf+vbL6fJ7feYu/qe9iTyG8EgXf1rKTv5QMhxHEJXb3GjGhHfeFTF0bf\n+v63UjLzdE3YrB01QyK1JU2xWyX15GZ2bvwrjbWLmdd2zHMiUVMRykXXMJLpYtn8cxDNbWGXqkyq\n9HhALBF2qaaSqorcr0KqDCUkVhWXvtxIhtuu/6t328//5gnEL23L/g8pZe9B2mWz+CdDCLFQi0S/\nKKW8uP2sS/X0qteqSRnFtNxywd9FZLMM969nbCL0aIhG0jTVLSGdbEdR9jUOOliwnTxdu+/DKk0w\nb86JlFYeib9IY8HSDCsaAlY1uCyvlTRGOmHPU8gNm/jFjx9k01MW/bsC+btSdylA3lXC/7iUcsML\ntqEvIl42ZKoCIUQqEjU+Cnxo4eFzlUvec3Gy86SjmXAVRssdqYkJo0ykdLIZk0h2OonKD3ezo/ch\nErEGFs49Fe0gVEUDGbBj9/2M53ppXXk+4yuXs3B5hiPnljim0efoeofm6ELofYKe2x7i8qtuk1uH\nrWLB9fstvH8HflfOKZjFywxCCEMI5Z2qanwuZqYTK5ZclOxsP4HnOlP1fGE7BbbuvIMg8Fgy/yyK\n81oZWFBD56IJls2xWFkvOaLOoiPeQKzkIrs3UFqzlY9+6wFu2jpsZVw37xN8wUP+SEpZ+qf8E7N4\nQVEmVa9NKNoXTE2de+XSuZH+oKBe89ZjSS6qnTScSDVVDSc2bt/MD75/G2/9xJsYKun0FURIpHJq\nlUjZYyqpMYvEqMXm+6/l2CMuf96L2SDw8XwHIQSua2HosWqnq394A+u3/U9uZKxLCCG+5/n2V6SU\n4wdhF83iEIQQ4uTa2uSXXNc99vy3vUqZf/FrjJ1OK9s31dK4voc9a27G1OMs6XzVQb/+ep7N5h1/\nRwiFRYvPxU4nKKQMSnGjOuQfi3vVgN1I1CMa86u26pUA3sHNO7jn+v8prr/rcUU39N9aueJVLxXH\ns1kcOIQQSzUz/oXAcy+Y03mSXNB8bDSX7cO2c+h6lNbGw6mrmfe8r5PPBUHgsaP3IcazPXS0rMI5\n6ST0ZbB8WYbjGmFlg8W8RD0DG3bwzS/+0r72T+vQUB7Iud5HX4pzUU+Hlx2ZqkAIYQKXRuKRT6um\n0XH4xa+Ktp1+mioSndXAXZGTJDIlYjmHWNYhN9TFjt4HqE3NZX7HCSjKwY/h8n2HLTvvwPdd0q96\nAxyZYMUR4xzXEMC2ddz6m3XWH35/r1AFt+ct9yvAfbOdqFcGRHg1fI2uRT4lYfXCOSerC+ecatSn\n5/9TulW2k2fLjtvRVINFC89maFEjcqnKgiUZVjdJVteXsHYNctPPH3b+7/V/8wLPfypnuV8EbpnN\nOXvlQAhxfFxT/t2Hsy8+bm7wzn9ZHXvV+SvR6kLnPldT6R3ezVWf/RX/63NXMOJFq0RquCgYGwmJ\nVDZjEh+zSWZKTKy/jygGrY2HP6dt6ht6kqGRLSydfxbbeu4hGWuioXY+AyObiZo1dO2+LxgY2eQE\ngdfv+fYXgF9JKZ85aGoWLwsIIZZopvFvgS/fWtN2mN+o1Cd1Lcrhi88/4NyyA0WuMMTmHX+nteFw\nGjuPKRMqnULSxEloYXdqL1LlW+P03fOg3PDH23PZ/mHbc7yrZRD8REo5swPRLF5WEEK0CKG+Xwjx\noZpEq1w6/+zUvLbjnpd072BByoDdA2sZHNnM3HknkT3leBoWj2LsvJctt9ye3bZ+pyLgx6WSe42U\nsuefvb0vBF62ZKqCcgV1lR6LXhl4wRVGXXNQv+LsRFvrSrXRaCGRcykOdtPVez+1yQ7md5z0grZH\nKyhYo2zq/juBpjJeo5TGN94RqCIYcq3SDzzX/6WUsu8F34hZHLIQQixSVePtilCv1LVIfMGck405\nLauM+toFKC9yBSpXGGTrzjtJJ9tpW3Qqm9ITOKP3uoMP3lFycnlbyOC6YsG5Tkq56UXdsFkcUhBC\nNOuqcnkyYb4vQLS/+bLTufB1J0eXHtXA56/6NW///99ATq9h0FIYKEK/BePjJpnRCNmMAWOSxESJ\nxEiBzQ/9jGNXXHHA25DND9I7uJbF886sGgVIKcnm++n9f+3d329cRxUH8O+ZuXN37+7dXXv9265j\np3EhiRtCHBpRIQS0Vdq+VaRq/wde+GeQeOAvAFIQvJBEKiBEQcQkamJCmsQhv1r/2qz39497584M\nD7shIQLVMXFN7fOR9nH33isdzZ4zZ2bu+hX7jwd/bNRb61IK72exbv8EwF94smr/IqIcgO+nVPjD\nxMZHp8cX9MzkK+HEyMvwVfC53/9ffLp+Baulv+Grs69BjR1AO+ejk1Vo51PQgYSMV9C4ddFtLP6u\nXlm+mVJB+nxUb/wYwIe8j2//IiIfwNspP/eDJOl+Z2z4cHRw6pv5ydGvIUj/9/c+fhE6UR1XP/kl\n1h5ejxrtEokgczFp1X6E3gRre1dvboft+WLqSf09Kq8L6Z8honckedlsMOQPDcx683NvIx9O7mgH\nwNoEm7X7KFWWsbKx1Fwr/V0J4d3VSfengDvLbyZnT+tPBpwSJN+V0j/jnJuYGjtmJ0bmMyODcyjk\nX9jR4so5i1pjBaXKbdz77GJ3vXxDEImSdclZa5OzAP7Ey0/Z04josBD0bpgL3m+3o8PHT81FJ147\nmR2d/wrU7CwqxkO51ltmXa/6aG0qhLUIYbWLjUu/wVj+IAYLB57pmo+OMp8eX0C7W0WpcgvrD2/E\nD9Yux0nSiR3wK2PiDwBc+LKeGMV2DhFNATjjq8z7SRJ/ozgw050eP5EbGZyjocEXobz0c7+msQlu\n3vkQiYkwM/0qVuNVbFRuJqv3FttRsyxJygs26pwF8Gs+VII9jYgGALyjvOA9Y/V3c5lRPT1xMhwt\nviSGi4d2vMsa6zYeVm5jY/OWfbB6uVlrrqSk8D/SSfvnAD5wzpV29Ab+j+yrYupJ/ST1CEBvpfzs\naWPiU4BIDxamu0MDB8OB3JQq5CYRZoaRThUgn2HJn066/zpWstZcQaX2oFOu3enWG6uhlKkVInwU\n6/Z5AOeccxs79pBszyGiAwDe8lX2TefMq9aaYiE31R4amM0M5qdTvZgdRZAuPNNeP2NidKIamq0S\nqs0VVOufxeXqnVa1/mmGhKgJkn+OdetRzN7ZsQdkew4RDQM4ncll3rTAt+NO9EJ+aryZn51LpcYO\nZRC+iJw/hkGbRa7SxfLHv8DC0fc+93etTdCJ6mh1yqg1VrB8/w+WSNQrtfu+dUZ70l+MdfuCc/Y8\ngCXuQLGtIqIsgNc9L31akPyeTrovZYOh1tDAjFcszISFcBL5cBxBehC+ymx5EtY6iyiqo9XZRL25\nilpjxZZrdxvl6h2pdUd5XvpqYqJz1ibn0Oua8nJptiX9jtW3pFCnPZl6QyedY6lULioWZjBUmM0V\nclOUD8eRCYpI++GW91g55xDpJjqdCmrNNdSaK26zerexWbvnOt1q4HnBdWv1bxMTXQDw+/26T3rf\nFlP/CRFNAzgB0JGUH55yzh6xVo8bo/NSKu37Yay8NEmhnJTKEUlYq8mYBMbGFOsOYt1KO2chhapI\nqe4755Zi3boE4DqAS3xCFHue+onqSQCHezGLeWv1lLF6QJC0vp+NlBegH7MQwnPOGhiryRhNOum4\nKG6mrDWelKoihLdKJK5FcWMRvZi97Jxb392nZHtJf2nVAoCjXiZccJDHkcTTVsdFAMJXmW7Kz1op\nfEjpQQjlnDMwRpOxmpIkclHcVMbEvpCqLoVaJxI3ori5CLhrAD4GcI+LJ/a89Pdgfx3AvPLSx4VQ\nC9bqg8YmRees76tMx1dZK6Xq5QdCOQdHxmhYqykxsYvipqeTbloKrymEKknhLcdJ+6/WJksAlgB8\nwl1+9rwQkQQwD+CYFOpl5QWvGKsPWZsMW5sESgVdX2WNJ1MkZS9mAfRyA6vJJLGLdEtq3Q5IeB0p\nvE0pvNuJiS8nJroC4BqAq9zl7+Fiagv6BwMUAYwByABIAwgAKADdJz4NAKsAmvxHznZTv/NaADAO\nIEQvZtMAUgBiAB30YrYFYA1AlWOW7TYiCgFMAMihN8Y+iluNx+NsG8A6gDInn2y3EVGAXm5QRG98\nfZQfGPx7flACsMH7ndhuIyIFYBTACB6PsWkAAo9zgy6AMoB151y0S7f6pcHFFGOMMcYYY4xtwxd/\nMD1jjDHGGGOM7QFcTDHGGGOMMcbYNnAxxRhjjDHGGGPbwMUUY4wxxhhjjG0DF1OMMcYYY4wxtg3/\nBC2BkOKlK9LcAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 1080x432 with 10 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"EzbVkDAwUmfa","colab_type":"code","outputId":"ca5312d0-c969-4c7e-d88d-8f98f889d5ee","executionInfo":{"status":"ok","timestamp":1576426176170,"user_tz":-60,"elapsed":84124,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":84}},"source":["# Accuracy over the testing set\n","predictions = best_model.predict(test_features)\n","predictions = np.round(predictions)\n","\n","score = np.array(best_model.evaluate(test_features, test_labels, verbose=0, sample_weight=test_weights))\n","print(\"Model performance on test set:\\t[ Loss: {}\\tAccuracy: {} ]\".format(*score.round(4)))\n","print(\"\\nPredictions: {}\\nSolutions:   {}\".format(list(map(int, predictions))[:50], list(map(int, test_labels))[:50]))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Model performance on test set:\t[ Loss: 0.416\tAccuracy: 0.7531 ]\n","\n","Predictions: [0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1]\n","Solutions:   [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"DVKxqyKZpyg8","colab_type":"code","outputId":"976a8aed-3806-4fae-a569-a0af6894ebe0","executionInfo":{"status":"ok","timestamp":1576426176733,"user_tz":-60,"elapsed":84674,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":392}},"source":["# Weighted confusion matrix (noP300: 80%, P300: 20%)\n","data = confusion_matrix(y_true=test_labels, y_pred=predictions, sample_weight=test_weights)\n","\n","# Normalized confusion matrix (values in range 0-1)\n","data_norm = data/np.full(data.shape, len(test_labels))\n","\n","# Plot the confusion matrix\n","df_cm = pd.DataFrame(data_norm, columns=np.unique(test_labels), index = np.unique(test_labels))\n","df_cm.index.name = 'Actual'\n","df_cm.columns.name = 'Predicted'\n","plt.figure(figsize = (6,5))\n","sns.set(font_scale = 1.4)\n","cm = sns.heatmap(df_cm, cmap=\"Blues\", annot=True, annot_kws = {\"size\": 16}, vmin=0, vmax=1)\n","cm.axes.set_title(\"CNN2a confusion matrix\\n\", fontsize=20)\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYgAAAF3CAYAAAC/h9zqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBQgREYmlACEiIrH+H8mk/246KpbbAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x360 with 2 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"JUPsdxe4qrqi","colab_type":"code","colab":{}},"source":["# Model metrics (sens, spec, ppv, npv)\n","def model_metrics(conf_matrix):\n","    tn, fp, fn, tp = list(data_norm.flatten())\n","    sens = round(tp/(tp+fn),4) # Sensitivity\n","    spec = round(tn/(tn+fp),4) # Specificity\n","    ppv = round(tp/(tp+fp),4) # Positive Predicted Value\n","    npv = round(tn/(tn+fn),4) # Negative Predicted Value\n","    return {\"Sensitivity\":sens, \"Specificity\":spec, \"PPV\":ppv, \"NPV\":npv}"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"yo3CmAb2qrt6","colab_type":"code","outputId":"242b6cb7-0602-4515-d8a9-0e37c4ec166d","executionInfo":{"status":"ok","timestamp":1576426176735,"user_tz":-60,"elapsed":84647,"user":{"displayName":"Lorenzo Gualniera","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mCLpjQsro-Xs6X0aZX_0HuLLblFWB6HZFMeyZLy=s64","userId":"15976103540540623653"}},"colab":{"base_uri":"https://localhost:8080/","height":85}},"source":["# Put model metrics into a table\n","metrics = model_metrics(data_norm)\n","\n","# Create figure\n","fig = plt.figure(figsize=(5,1))\n","ax = fig.add_subplot(111)\n","\n","# Hide graph outlines\n","for item in [fig, ax]:\n","    item.patch.set_visible(False)\n","ax.xaxis.set_visible(False)\n","ax.yaxis.set_visible(False)\n","ax.spines[\"top\"].set_visible(False)\n","ax.spines[\"bottom\"].set_visible(False)\n","ax.spines[\"left\"].set_visible(False)\n","ax.spines[\"right\"].set_visible(False)\n","\n","# Table definition\n","table = ax.table(cellText=[list(metrics.values())], \n","                     colLabels=list(metrics.keys()),\n","                     loc=\"center\",\n","                     cellLoc=\"center\",\n","                     colColours=[\"c\"]*4)\n","table.set_fontsize(16)\n","table.scale(2,2)\n","\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAjwAAABECAYAAACF4e8fAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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size 360x72 with 1 Axes>"]},"metadata":{"tags":[]}}]}]}